The dynamic nature of information often makes it difficult and costly to maintain, and typically due to constant change, the data intrinsically lacks complete accuracy. Most systems require absolute data, which is often difficult to obtain and misleading because it lacks absolute accuracy. In addition, the various systems of an organization typically contain “application firewalls” between the systems. Each of these disparate systems is geared toward different user constituencies or business functions, and although much of the data originates from the same source, the disparate systems effectively contribute to data corruption. Furthermore, systems are usually architected to limit the data from self-learning. Fixed relationships pre-define context, and such relationships do not respond well to divergent user needs. Such data compartmentalization often undermines organizational performance by, for example, limiting insight into the impacts of decision-making across planes of significance (e.g., cost, revenue, customer outcome) and contributing little to the understanding of how the downstream impact of such decisions greatly affects the bottom line.
Therefore, a need exists for a system and method for processing information based upon probabilistic significance that varies with a variety of factors such as time, user, industry, external events and the value of the data itself. In order to promote sophisticated understanding of information and its impact on a given organization, a need exists for a system that combines the use of sophisticated object data models, probabilistic modeling, statistical analysis and artificial intelligence techniques to create intelligent, self-learning data objects, and to derive relevant and insightful results for a multitude of end-user constituencies.